#315 Clamp smoothed values to minimum of 0

scripts/pipeline.py

@@ -2,6 +2,7 @@
 import pickle
 from os import makedirs, path
 
+import numpy as np
 from pandas import read_pickle
 from scipy.signal import savgol_filter
 from tqdm import tqdm
@@ -208,10 +209,11 @@ def pickle_object(short_name, obj):
 
             self.__timeseries_quarterly.append(quarterly_values)
             smooth_series = savgol_filter(quarterly_values, 9, 2, mode='nearest')
+            smooth_series_no_negatives = np.clip(smooth_series, a_min=0, a_max=None)
 
             # _, _1, smooth_series_s, _2 = SteadyStateModel(quarterly_values).run_smoothing()
             # smooth_series = smooth_series_s[0].tolist()[0]
-            self.__timeseries_quarterly_smoothed.append(smooth_series)
+            self.__timeseries_quarterly_smoothed.append(smooth_series_no_negatives)
 
         em = Emergence(all_quarterly_values)
         for term_index in tqdm(range(self.__term_counts_per_week.shape[1]), unit='term', desc='Calculating eScore',

tests/algorithms/test_holt_winters.py

@@ -0,0 +1,62 @@
+from sys import platform as sys_pf
+
+from scripts.algorithms.holtwinters_predictor import HoltWintersPredictor
+
+if sys_pf == 'darwin':
+    import matplotlib
+
+    matplotlib.use("TkAgg")
+
+import unittest
+import numpy.testing as np_test
+
+import platform;
+
+print(platform.platform())
+import sys;
+
+print("Python", sys.version)
+import numpy as np;
+
+print("NumPy", np.__version__)
+import scipy;
+
+print("SciPy", scipy.__version__)
+import sklearn;
+
+print("Scikit-Learn", sklearn.__version__)
+import statsmodels;
+
+print("Statsmodels", statsmodels.__version__)
+
+
+class HoltWintersTests(unittest.TestCase):
+
+    def test_negatives_in_sequence(self):
+        time_series = [1, 1, -1, 1, 1]
+        num_predicted_periods = 3
+
+        with self.assertRaises(NotImplementedError) as nie:
+            HoltWintersPredictor(time_series, num_predicted_periods)
+
+        self.assertEqual(nie.exception.args[0], 'Unable to correct for negative or zero values')
+
+    def test_zeros_in_sequence(self):
+        time_series = [1, 1, 0, 1, 1]
+        num_predicted_periods = 3
+        expected_prediction = [0.8] * num_predicted_periods
+        hw = HoltWintersPredictor(time_series, num_predicted_periods)
+
+        actual_prediction = hw.predict_counts()
+
+        np_test.assert_almost_equal(actual_prediction, expected_prediction, decimal=4)
+
+    def test_static_sequence(self):
+        time_series = [1.0, 1.0, 1.0, 1.0, 1.0]
+        num_predicted_periods = 3
+        expected_prediction = [1] * num_predicted_periods
+        hw = HoltWintersPredictor(time_series, num_predicted_periods)
+
+        actual_prediction = hw.predict_counts()
+
+        np_test.assert_almost_equal(actual_prediction, expected_prediction, decimal=4)